


import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__			:      List[Any]								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[Any]								={
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}







class 	__lowercase    (__lowerCAmelCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						'''conditional_detr'''
							_UpperCAmelCase      =						['''past_key_values''']
							_UpperCAmelCase      =						{
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}






							def __init__(      self			,						lowerCAmelCase__=True			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3			,						lowerCAmelCase__=3_0_0			,						lowerCAmelCase__=6			,						lowerCAmelCase__=2_0_4_8			,						lowerCAmelCase__=8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=2_0_4_8			,						lowerCAmelCase__=8			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=True			,						lowerCAmelCase__="relu"			,						lowerCAmelCase__=2_5_6			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1.0			,						lowerCAmelCase__=False			,						lowerCAmelCase__="sine"			,						lowerCAmelCase__="resnet50"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=2			,						lowerCAmelCase__=1			,						lowerCAmelCase__=1			,						lowerCAmelCase__=2			,						lowerCAmelCase__=5			,						lowerCAmelCase__=2			,						lowerCAmelCase__=0.25			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if backbone_config is not None and use_timm_backbone:
													raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'       )

										if not use_timm_backbone:
													if backbone_config is None:
																logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'       )
																SCREAMING_SNAKE_CASE_		:     List[str]					=					CONFIG_MAPPING['resnet'](out_features=['stage4']       )
													elif isinstance(_lowerCamelCase			,						_lowerCamelCase       ):
																SCREAMING_SNAKE_CASE_		:     Tuple					=					backbone_config.get('model_type'       )
																SCREAMING_SNAKE_CASE_		:     Any					=					CONFIG_MAPPING[backbone_model_type]
																SCREAMING_SNAKE_CASE_		:     int					=					config_class.from_dict(_lowerCamelCase       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					use_timm_backbone
										SCREAMING_SNAKE_CASE_		:     Dict					=					backbone_config
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					num_channels
										SCREAMING_SNAKE_CASE_		:     str					=					num_queries
										SCREAMING_SNAKE_CASE_		:     List[str]					=					d_model
										SCREAMING_SNAKE_CASE_		:     int					=					encoder_ffn_dim
										SCREAMING_SNAKE_CASE_		:     Any					=					encoder_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					encoder_attention_heads
										SCREAMING_SNAKE_CASE_		:     Any					=					decoder_ffn_dim
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					decoder_layers
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					decoder_attention_heads
										SCREAMING_SNAKE_CASE_		:     Any					=					dropout
										SCREAMING_SNAKE_CASE_		:     Tuple					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					activation_dropout
										SCREAMING_SNAKE_CASE_		:     str					=					activation_function
										SCREAMING_SNAKE_CASE_		:     str					=					init_std
										SCREAMING_SNAKE_CASE_		:     Any					=					init_xavier_std
										SCREAMING_SNAKE_CASE_		:     Dict					=					encoder_layerdrop
										SCREAMING_SNAKE_CASE_		:     Dict					=					decoder_layerdrop
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					encoder_layers
										SCREAMING_SNAKE_CASE_		:     str					=					auxiliary_loss
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     int					=					backbone
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_pretrained_backbone
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					dilation
										# Hungarian matcher
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					class_cost
										SCREAMING_SNAKE_CASE_		:     List[str]					=					bbox_cost
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					giou_cost
										# Loss coefficients
										SCREAMING_SNAKE_CASE_		:     str					=					mask_loss_coefficient
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					dice_loss_coefficient
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					cls_loss_coefficient
										SCREAMING_SNAKE_CASE_		:     int					=					bbox_loss_coefficient
										SCREAMING_SNAKE_CASE_		:     List[str]					=					giou_loss_coefficient
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase			,						**_lowerCamelCase       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.encoder_attention_heads






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return self.d_model






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					copy.deepcopy(self.__dict__       )
										if self.backbone_config is not None:
													SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.backbone_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.__class__.model_type
										return output







class 	__lowercase    (__lowerCAmelCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						version.parse("""1.11"""  )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return OrderedDict(
										    [
										        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
										        ('pixel_mask', {0: 'batch'}),
										    ]       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1E-5






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1_2






"""simple docstring"""


from __future__ import annotations

import math





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if len(__lowerCAmelCase							) != 2 or len(a[0]							) != 2 or len(__lowerCAmelCase							) != 2 or len(b[0]							) != 2:
												raise Exception("Matrices are not 2x2"							)
						lowerCamelCase__   =[
						    [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
						    [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
						]
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						return [
						    [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]							)							)]
						    for row in range(len(__lowerCAmelCase							)							)
						]





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[list, list, list, list]:




						'''simple docstring'''



						if len(__lowerCAmelCase							) % 2 != 0 or len(a[0]							) % 2 != 0:
												raise Exception("Odd matrices are not supported!"							)

						lowerCamelCase__   =len(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_length // 2

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[
						    [a[i][j] for j in range(__lowerCAmelCase  ,       __lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)
						]

						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase							)]
						lowerCamelCase__   =[[a[i][j] for j in range(__lowerCAmelCase							)] for i in range(__lowerCAmelCase  ,       __lowerCAmelCase							)]

						return top_left, top_right, bot_left, bot_right





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   tuple[int, int]:




						'''simple docstring'''



						return len(__lowerCAmelCase							), len(matrix[0]							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						print("\n".join(str(__lowerCAmelCase							) for line in matrix							)							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							) == (2, 2):
												return default_matrix_multiplication(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   , lowerCamelCase__   =split_matrix(__lowerCAmelCase							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)
						lowerCamelCase__   =actual_strassen(matrix_subtraction(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)							)

						lowerCamelCase__   =matrix_addition(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =matrix_subtraction(matrix_subtraction(matrix_addition(__lowerCAmelCase  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)  ,       __lowerCAmelCase							)

						# construct the new matrix from our 4 quadrants
						lowerCamelCase__   =[]
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(top_left[i] + top_right[i]							)
						for i in range(len(__lowerCAmelCase							)							):
												new_matrix.append(bot_left[i] + bot_right[i]							)
						return new_matrix





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   list:




						'''simple docstring'''



						if matrix_dimensions(__lowerCAmelCase							)[1] != matrix_dimensions(__lowerCAmelCase							)[0]:
												lowerCamelCase__   =(
												    "Unable to multiply these matrices, please check the dimensions.\n"
												    F'''Matrix A: {matrixa}\n'''
												    F'''Matrix B: {matrixa}'''
												)
												raise Exception(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)
						lowerCamelCase__   =matrix_dimensions(__lowerCAmelCase							)

						if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
												return [matrixa, matrixa]

						lowerCamelCase__   =max(*__lowerCAmelCase  ,       *__lowerCAmelCase							)
						lowerCamelCase__   =int(math.pow(2  ,       math.ceil(math.loga(__lowerCAmelCase							)							)							)							)
						lowerCamelCase__   =matrixa
						lowerCamelCase__   =matrixa

						# Adding zeros to the matrices so that the arrays dimensions are the same and also
						# power of 2
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								new_matrixa[i].append(0							)
												else:
																		new_matrixa.append([0] * maxim							)

						lowerCamelCase__   =actual_strassen(__lowerCAmelCase  ,       __lowerCAmelCase							)

						# Removing the additional zeros
						for i in range(0  ,       __lowerCAmelCase							):
												if i < dimensiona[0]:
																		for _ in range(dimensiona[1]  ,       __lowerCAmelCase							):
																								final_matrix[i].pop()
												else:
																		final_matrix.pop()
						return final_matrix


if __name__ == "__main__":
	a				=[
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 4, 3, 1],
	    [2, 3, 6, 7],
	    [3, 1, 2, 4],
	    [2, 3, 4, 5],
	    [6, 2, 3, 1],
	]
	a				=[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
	print(strassen(matrixa, matrixa))


